Overview

Dataset statistics

Number of variables27
Number of observations400
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory87.5 KiB
Average record size in memory224.0 B

Variable types

Numeric23
Categorical4

Alerts

Word has a high cardinality: 400 distinct values High cardinality
Sentence has a high cardinality: 400 distinct values High cardinality
Concreteness is highly correlated with AoAHigh correlation
A priori Predictability is highly correlated with similarity and 3 other fieldsHigh correlation
OLD20 is highly correlated with #letters and 1 other fieldsHigh correlation
#letters is highly correlated with OLD20 and 1 other fieldsHigh correlation
OrthNeighSize is highly correlated with OLD20 and 1 other fieldsHigh correlation
BigramFreq is highly correlated with TrigramFreqHigh correlation
TrigramFreq is highly correlated with BigramFreqHigh correlation
Frequency is highly correlated with LogFreq(Zipf)High correlation
LogFreq(Zipf) is highly correlated with FrequencyHigh correlation
similarity is highly correlated with A priori Predictability and 1 other fieldsHigh correlation
AoA is highly correlated with ConcretenessHigh correlation
cloze is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
Plausibility is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
Predictability is highly correlated with A priori Predictability and 3 other fieldsHigh correlation
PRECEDING_Frequency is highly correlated with PRECEDING_LogFreq(Zipf) and 1 other fieldsHigh correlation
PRECEDING_LogFreq(Zipf) is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
LENprec is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
Concreteness is highly correlated with AoAHigh correlation
A priori Predictability is highly correlated with cloze and 2 other fieldsHigh correlation
BLP_rt is highly correlated with BLP_accuracy and 1 other fieldsHigh correlation
BLP_accuracy is highly correlated with BLP_rtHigh correlation
OLD20 is highly correlated with #letters and 1 other fieldsHigh correlation
#letters is highly correlated with OLD20 and 1 other fieldsHigh correlation
OrthNeighSize is highly correlated with OLD20 and 1 other fieldsHigh correlation
BigramFreq is highly correlated with TrigramFreqHigh correlation
TrigramFreq is highly correlated with BigramFreqHigh correlation
Frequency is highly correlated with LogFreq(Zipf)High correlation
LogFreq(Zipf) is highly correlated with BLP_rt and 1 other fieldsHigh correlation
similarity is highly correlated with PredictabilityHigh correlation
AoA is highly correlated with ConcretenessHigh correlation
cloze is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
Plausibility is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
Predictability is highly correlated with A priori Predictability and 3 other fieldsHigh correlation
PRECEDING_Frequency is highly correlated with PRECEDING_LogFreq(Zipf) and 1 other fieldsHigh correlation
PRECEDING_LogFreq(Zipf) is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
LENprec is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
A priori Predictability is highly correlated with cloze and 2 other fieldsHigh correlation
OLD20 is highly correlated with #letters and 1 other fieldsHigh correlation
#letters is highly correlated with OLD20 and 1 other fieldsHigh correlation
OrthNeighSize is highly correlated with OLD20 and 1 other fieldsHigh correlation
Frequency is highly correlated with LogFreq(Zipf)High correlation
LogFreq(Zipf) is highly correlated with FrequencyHigh correlation
cloze is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
Plausibility is highly correlated with A priori Predictability and 1 other fieldsHigh correlation
Predictability is highly correlated with A priori Predictability and 1 other fieldsHigh correlation
PRECEDING_Frequency is highly correlated with PRECEDING_LogFreq(Zipf) and 1 other fieldsHigh correlation
PRECEDING_LogFreq(Zipf) is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
LENprec is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
Concreteness is highly correlated with AoAHigh correlation
SensorimotorStrength is highly correlated with AoAHigh correlation
A priori Predictability is highly correlated with cloze and 2 other fieldsHigh correlation
BLP_rt is highly correlated with BLP_accuracy and 2 other fieldsHigh correlation
BLP_accuracy is highly correlated with BLP_rt and 1 other fieldsHigh correlation
OLD20 is highly correlated with #letters and 1 other fieldsHigh correlation
#letters is highly correlated with OLD20 and 1 other fieldsHigh correlation
OrthNeighSize is highly correlated with OLD20 and 1 other fieldsHigh correlation
BigramFreq is highly correlated with TrigramFreqHigh correlation
TrigramFreq is highly correlated with BigramFreqHigh correlation
Frequency is highly correlated with LogFreq(Zipf)High correlation
LogFreq(Zipf) is highly correlated with BLP_rt and 2 other fieldsHigh correlation
AoA is highly correlated with Concreteness and 2 other fieldsHigh correlation
cloze is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
Plausibility is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
Predictability is highly correlated with A priori Predictability and 2 other fieldsHigh correlation
PRECEDING_Frequency is highly correlated with PRECEDING_LogFreq(Zipf) and 1 other fieldsHigh correlation
PRECEDING_LogFreq(Zipf) is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
LENprec is highly correlated with PRECEDING_Frequency and 1 other fieldsHigh correlation
ID is uniformly distributed Uniform
Word is uniformly distributed Uniform
Sentence is uniformly distributed Uniform
A priori Predictability is uniformly distributed Uniform
ID has unique values Unique
Word has unique values Unique
SensorimotorStrength has unique values Unique
Sentence has unique values Unique
BigramFreq has unique values Unique
TrigramFreq has unique values Unique
similarity has unique values Unique
Predictability has unique values Unique
OrthNeighSize has 121 (30.2%) zeros Zeros
cloze has 125 (31.2%) zeros Zeros

Reproduction

Analysis started2022-07-01 13:22:56.247914
Analysis finished2022-07-01 13:23:58.318952
Duration1 minute and 2.07 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.5
Minimum100
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:23:58.423700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile119.95
Q1199.75
median299.5
Q3399.25
95-th percentile479.05
Maximum499
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143013
Coefficient of variation (CV)0.3860243783
Kurtosis-1.2
Mean299.5
Median Absolute Deviation (MAD)100
Skewness0
Sum119800
Variance13366.66667
MonotonicityStrictly increasing
2022-07-01T14:23:58.538364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4991
 
0.2%
2361
 
0.2%
2261
 
0.2%
2271
 
0.2%
2281
 
0.2%
2291
 
0.2%
2301
 
0.2%
2311
 
0.2%
2321
 
0.2%
2331
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
1001
0.2%
1011
0.2%
1021
0.2%
1031
0.2%
1041
0.2%
1051
0.2%
1061
0.2%
1071
0.2%
1081
0.2%
1091
0.2%
ValueCountFrequency (%)
4991
0.2%
4981
0.2%
4971
0.2%
4961
0.2%
4951
0.2%
4941
0.2%
4931
0.2%
4921
0.2%
4911
0.2%
4901
0.2%

Word
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
council
 
1
prank
 
1
cable
 
1
launch
 
1
sample
 
1
Other values (395)
395 

Length

Max length7
Median length6
Mean length5.5075
Min length4

Characters and Unicode

Total characters2203
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique400 ?
Unique (%)100.0%

Sample

1st rowabsence
2nd rowaccent
3rd rowaccess
4th rowaction
5th rowadult

Common Values

ValueCountFrequency (%)
council1
 
0.2%
prank1
 
0.2%
cable1
 
0.2%
launch1
 
0.2%
sample1
 
0.2%
lift1
 
0.2%
sonnet1
 
0.2%
cellar1
 
0.2%
motive1
 
0.2%
method1
 
0.2%
Other values (390)390
97.5%

Length

2022-07-01T14:23:58.652059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cast1
 
0.2%
blast1
 
0.2%
autumn1
 
0.2%
review1
 
0.2%
ideal1
 
0.2%
scandal1
 
0.2%
verdict1
 
0.2%
cactus1
 
0.2%
mash1
 
0.2%
noise1
 
0.2%
Other values (390)390
97.5%

Most occurring characters

ValueCountFrequency (%)
e295
13.4%
a182
 
8.3%
r174
 
7.9%
t152
 
6.9%
o144
 
6.5%
c129
 
5.9%
s129
 
5.9%
l127
 
5.8%
n123
 
5.6%
i113
 
5.1%
Other values (16)635
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2203
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e295
13.4%
a182
 
8.3%
r174
 
7.9%
t152
 
6.9%
o144
 
6.5%
c129
 
5.9%
s129
 
5.9%
l127
 
5.8%
n123
 
5.6%
i113
 
5.1%
Other values (16)635
28.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2203
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e295
13.4%
a182
 
8.3%
r174
 
7.9%
t152
 
6.9%
o144
 
6.5%
c129
 
5.9%
s129
 
5.9%
l127
 
5.8%
n123
 
5.6%
i113
 
5.1%
Other values (16)635
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e295
13.4%
a182
 
8.3%
r174
 
7.9%
t152
 
6.9%
o144
 
6.5%
c129
 
5.9%
s129
 
5.9%
l127
 
5.8%
n123
 
5.6%
i113
 
5.1%
Other values (16)635
28.8%

Concreteness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct202
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2471
Minimum1.19
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:23:58.750823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.19
5-th percentile1.5985
Q12.26
median3.075
Q34.4
95-th percentile4.96
Maximum5
Range3.81
Interquartile range (IQR)2.14

Descriptive statistics

Standard deviation1.154878761
Coefficient of variation (CV)0.3556646734
Kurtosis-1.315021753
Mean3.2471
Median Absolute Deviation (MAD)0.905
Skewness0.1174310161
Sum1298.84
Variance1.333744952
MonotonicityNot monotonic
2022-07-01T14:23:58.864519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511
 
2.8%
4.911
 
2.8%
4.867
 
1.8%
4.967
 
1.8%
4.937
 
1.8%
2.897
 
1.8%
1.965
 
1.2%
3.95
 
1.2%
3.865
 
1.2%
1.974
 
1.0%
Other values (192)331
82.8%
ValueCountFrequency (%)
1.191
 
0.2%
1.331
 
0.2%
1.341
 
0.2%
1.371
 
0.2%
1.411
 
0.2%
1.441
 
0.2%
1.451
 
0.2%
1.471
 
0.2%
1.52
0.5%
1.524
1.0%
ValueCountFrequency (%)
511
2.8%
4.974
 
1.0%
4.967
1.8%
4.937
1.8%
4.922
 
0.5%
4.911
 
0.2%
4.911
2.8%
4.892
 
0.5%
4.873
 
0.8%
4.867
1.8%

Valence
Real number (ℝ≥0)

Distinct237
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2222
Minimum1.68
Maximum8.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:23:58.977220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.68
5-th percentile2.6295
Q14.5
median5.475
Q36.15
95-th percentile7.142
Maximum8.05
Range6.37
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation1.34833385
Coefficient of variation (CV)0.2581926869
Kurtosis-0.1422011341
Mean5.2222
Median Absolute Deviation (MAD)0.77
Skewness-0.6154958252
Sum2088.88
Variance1.81800417
MonotonicityNot monotonic
2022-07-01T14:23:59.081936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.57
 
1.8%
66
 
1.5%
5.96
 
1.5%
5.956
 
1.5%
4.895
 
1.2%
5.295
 
1.2%
6.455
 
1.2%
5.865
 
1.2%
5.684
 
1.0%
5.164
 
1.0%
Other values (227)347
86.8%
ValueCountFrequency (%)
1.681
0.2%
1.791
0.2%
1.891
0.2%
1.911
0.2%
1.952
0.5%
21
0.2%
2.052
0.5%
2.111
0.2%
2.151
0.2%
2.291
0.2%
ValueCountFrequency (%)
8.051
0.2%
7.941
0.2%
7.811
0.2%
7.751
0.2%
7.721
0.2%
7.671
0.2%
7.631
0.2%
7.611
0.2%
7.531
0.2%
7.521
0.2%

Arousal
Real number (ℝ≥0)

Distinct219
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.160275
Minimum2.15
Maximum7.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:23:59.189648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile2.699
Q13.41
median4
Q34.86
95-th percentile6.061
Maximum7.05
Range4.9
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation0.9998018222
Coefficient of variation (CV)0.2403210899
Kurtosis-0.3079859129
Mean4.160275
Median Absolute Deviation (MAD)0.67
Skewness0.4901026229
Sum1664.11
Variance0.9996036836
MonotonicityNot monotonic
2022-07-01T14:23:59.299354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410
 
2.5%
3.456
 
1.5%
4.56
 
1.5%
4.056
 
1.5%
3.675
 
1.2%
3.25
 
1.2%
3.15
 
1.2%
3.055
 
1.2%
3.955
 
1.2%
3.95
 
1.2%
Other values (209)342
85.5%
ValueCountFrequency (%)
2.151
 
0.2%
2.191
 
0.2%
2.211
 
0.2%
2.241
 
0.2%
2.331
 
0.2%
2.352
0.5%
2.453
0.8%
2.481
 
0.2%
2.51
 
0.2%
2.531
 
0.2%
ValueCountFrequency (%)
7.051
0.2%
6.91
0.2%
6.851
0.2%
6.571
0.2%
6.551
0.2%
6.521
0.2%
6.431
0.2%
6.352
0.5%
6.311
0.2%
6.291
0.2%

SensorimotorStrength
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.629960794
Minimum1.919279474
Maximum7.223465009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:23:59.404072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.919279474
5-th percentile3.03245118
Q14.067771543
median4.697519846
Q35.297323439
95-th percentile6.147203697
Maximum7.223465009
Range5.304185535
Interquartile range (IQR)1.229551896

Descriptive statistics

Standard deviation0.9312108076
Coefficient of variation (CV)0.2011271475
Kurtosis0.1137164603
Mean4.629960794
Median Absolute Deviation (MAD)0.602883567
Skewness-0.1291562511
Sum1851.984318
Variance0.8671535682
MonotonicityNot monotonic
2022-07-01T14:23:59.513750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1794547411
 
0.2%
4.7032011381
 
0.2%
5.1177796481
 
0.2%
4.808694871
 
0.2%
5.3500377891
 
0.2%
4.8039554311
 
0.2%
5.5430755031
 
0.2%
3.0231706711
 
0.2%
6.1539195361
 
0.2%
6.1468502321
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
1.9192794741
0.2%
1.9226450111
0.2%
2.2596319991
0.2%
2.2900047861
0.2%
2.3030206741
0.2%
2.4589220921
0.2%
2.6002385241
0.2%
2.6262598631
0.2%
2.7538841151
0.2%
2.7590901841
0.2%
ValueCountFrequency (%)
7.2234650091
0.2%
7.0493648561
0.2%
6.9370797551
0.2%
6.9049501221
0.2%
6.8650757811
0.2%
6.8274045781
0.2%
6.7280953251
0.2%
6.6754260551
0.2%
6.5056321811
0.2%
6.4826829121
0.2%

Sentence
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
The bank refused my application for a loan for the second time.
 
1
Anyone found wandering the streets after curfew could face sanctions.
 
1
He has sensitive skin and soap gives him an awful itch on his hands.
 
1
After the relocation, she had to adapt to the new class and school.
 
1
Since late childhood, he has had a considerable complex about his looks.
 
1
Other values (395)
395 

Length

Max length94
Median length80
Mean length67.73
Min length45

Characters and Unicode

Total characters27092
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique400 ?
Unique (%)100.0%

Sample

1st rowThe school called because of the student's unauthorised absence for two consecutive days.
2nd rowPeople from Birmingham have the most recognizable accent from the United Kingdom.
3rd rowMen and women should have equal access to education and employment.
4th rowIt is time to turn ideas into action and make the plan happen.
5th rowAnyone over eighteen years of age counts as an adult according to the law.

Common Values

ValueCountFrequency (%)
The bank refused my application for a loan for the second time.1
 
0.2%
Anyone found wandering the streets after curfew could face sanctions.1
 
0.2%
He has sensitive skin and soap gives him an awful itch on his hands.1
 
0.2%
After the relocation, she had to adapt to the new class and school.1
 
0.2%
Since late childhood, he has had a considerable complex about his looks.1
 
0.2%
They became close friends after a quarrel they had on a bus.1
 
0.2%
There is a statue of King Henry VIII who is the founder of Trinity College in Cambridge.1
 
0.2%
On Sunday morning, he reported the theft of his car to the police.1
 
0.2%
Everybody at the park wore a colourful badge with their name on.1
 
0.2%
When doing gardening grandma wears an apron to protect her clothes.1
 
0.2%
Other values (390)390
97.5%

Length

2022-07-01T14:23:59.630437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the521
 
10.8%
a193
 
4.0%
of154
 
3.2%
to110
 
2.3%
in87
 
1.8%
and65
 
1.3%
is63
 
1.3%
his59
 
1.2%
for59
 
1.2%
was54
 
1.1%
Other values (1938)3468
71.8%

Most occurring characters

ValueCountFrequency (%)
4433
16.4%
e3000
11.1%
t1891
 
7.0%
a1740
 
6.4%
o1609
 
5.9%
r1416
 
5.2%
n1408
 
5.2%
i1379
 
5.1%
s1367
 
5.0%
h1360
 
5.0%
Other values (53)7489
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21665
80.0%
Space Separator4433
 
16.4%
Other Punctuation498
 
1.8%
Uppercase Letter479
 
1.8%
Decimal Number9
 
< 0.1%
Dash Punctuation6
 
< 0.1%
Currency Symbol2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3000
13.8%
t1891
 
8.7%
a1740
 
8.0%
o1609
 
7.4%
r1416
 
6.5%
n1408
 
6.5%
i1379
 
6.4%
s1367
 
6.3%
h1360
 
6.3%
l830
 
3.8%
Other values (16)5665
26.1%
Uppercase Letter
ValueCountFrequency (%)
T192
40.1%
H39
 
8.1%
A39
 
8.1%
S32
 
6.7%
I30
 
6.3%
W23
 
4.8%
C16
 
3.3%
M15
 
3.1%
B13
 
2.7%
D9
 
1.9%
Other values (14)71
 
14.8%
Decimal Number
ValueCountFrequency (%)
12
22.2%
22
22.2%
82
22.2%
51
11.1%
31
11.1%
01
11.1%
Other Punctuation
ValueCountFrequency (%)
.398
79.9%
,71
 
14.3%
'29
 
5.8%
Currency Symbol
ValueCountFrequency (%)
£1
50.0%
$1
50.0%
Space Separator
ValueCountFrequency (%)
4433
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22144
81.7%
Common4948
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3000
13.5%
t1891
 
8.5%
a1740
 
7.9%
o1609
 
7.3%
r1416
 
6.4%
n1408
 
6.4%
i1379
 
6.2%
s1367
 
6.2%
h1360
 
6.1%
l830
 
3.7%
Other values (40)6144
27.7%
Common
ValueCountFrequency (%)
4433
89.6%
.398
 
8.0%
,71
 
1.4%
'29
 
0.6%
-6
 
0.1%
12
 
< 0.1%
22
 
< 0.1%
82
 
< 0.1%
£1
 
< 0.1%
51
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII27091
> 99.9%
None1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4433
16.4%
e3000
11.1%
t1891
 
7.0%
a1740
 
6.4%
o1609
 
5.9%
r1416
 
5.2%
n1408
 
5.2%
i1379
 
5.1%
s1367
 
5.0%
h1360
 
5.0%
Other values (52)7488
27.6%
None
ValueCountFrequency (%)
£1
100.0%

A priori Predictability
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
0
200 
1
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0200
50.0%
1200
50.0%

Length

2022-07-01T14:23:59.731167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T14:23:59.820926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1200
50.0%
0200
50.0%

Most occurring characters

ValueCountFrequency (%)
1200
50.0%
0200
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1200
50.0%
0200
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1200
50.0%
0200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1200
50.0%
0200
50.0%

BLP_rt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct387
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean563.309475
Minimum485.65
Maximum762.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:23:59.900712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum485.65
5-th percentile507.7205
Q1532.7375
median555.35
Q3584.1
95-th percentile648.654
Maximum762.82
Range277.17
Interquartile range (IQR)51.3625

Descriptive statistics

Standard deviation42.95338759
Coefficient of variation (CV)0.07625184645
Kurtosis2.697739379
Mean563.309475
Median Absolute Deviation (MAD)25.42
Skewness1.273456667
Sum225323.79
Variance1844.993506
MonotonicityNot monotonic
2022-07-01T14:24:00.004434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
527.182
 
0.5%
550.422
 
0.5%
535.182
 
0.5%
592.082
 
0.5%
577.212
 
0.5%
584.162
 
0.5%
524.662
 
0.5%
581.032
 
0.5%
575.852
 
0.5%
564.032
 
0.5%
Other values (377)380
95.0%
ValueCountFrequency (%)
485.651
0.2%
491.931
0.2%
493.751
0.2%
495.551
0.2%
497.91
0.2%
498.321
0.2%
499.751
0.2%
500.361
0.2%
502.591
0.2%
503.661
0.2%
ValueCountFrequency (%)
762.821
0.2%
751.41
0.2%
746.171
0.2%
725.621
0.2%
690.591
0.2%
688.881
0.2%
685.331
0.2%
667.781
0.2%
667.131
0.2%
665.141
0.2%

BLP_accuracy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.971275
Minimum0.39
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:00.130099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.39
5-th percentile0.89
Q10.97
median0.98
Q31
95-th percentile1
Maximum1
Range0.61
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.05856790823
Coefficient of variation (CV)0.06030002649
Kurtosis42.15539432
Mean0.971275
Median Absolute Deviation (MAD)0.02
Skewness-5.576393994
Sum388.51
Variance0.003430199875
MonotonicityNot monotonic
2022-07-01T14:24:00.217892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1197
49.2%
0.9756
 
14.0%
0.9855
 
13.8%
0.9547
 
11.8%
0.9310
 
2.5%
0.928
 
2.0%
0.897
 
1.8%
0.96
 
1.5%
0.882
 
0.5%
0.872
 
0.5%
Other values (10)10
 
2.5%
ValueCountFrequency (%)
0.391
0.2%
0.481
0.2%
0.631
0.2%
0.661
0.2%
0.71
0.2%
0.711
0.2%
0.791
0.2%
0.81
0.2%
0.841
0.2%
0.851
0.2%
ValueCountFrequency (%)
1197
49.2%
0.9855
 
13.8%
0.9756
 
14.0%
0.9547
 
11.8%
0.9310
 
2.5%
0.928
 
2.0%
0.96
 
1.5%
0.897
 
1.8%
0.882
 
0.5%
0.872
 
0.5%

OLD20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.86175
Minimum1
Maximum3.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:00.323608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.15
Q11.65
median1.85
Q32
95-th percentile2.6525
Maximum3.45
Range2.45
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.4244258247
Coefficient of variation (CV)0.227971438
Kurtosis0.5518289366
Mean1.86175
Median Absolute Deviation (MAD)0.2
Skewness0.5344537383
Sum744.7
Variance0.1801372807
MonotonicityNot monotonic
2022-07-01T14:24:00.432288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1.941
 
10.2%
1.8533
 
8.2%
1.829
 
7.2%
1.6527
 
6.8%
1.726
 
6.5%
1.9523
 
5.8%
1.7521
 
5.2%
1.613
 
3.2%
1.4513
 
3.2%
1.512
 
3.0%
Other values (31)162
40.5%
ValueCountFrequency (%)
110
2.5%
1.054
 
1.0%
1.15
 
1.2%
1.153
 
0.8%
1.25
 
1.2%
1.251
 
0.2%
1.36
1.5%
1.357
1.8%
1.46
1.5%
1.4513
3.2%
ValueCountFrequency (%)
3.451
 
0.2%
3.21
 
0.2%
2.951
 
0.2%
2.855
1.2%
2.87
1.8%
2.753
0.8%
2.72
 
0.5%
2.657
1.8%
2.65
1.2%
2.556
1.5%

#letters
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
6
126 
5
96 
4
93 
7
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row6
3rd row6
4th row6
5th row5

Common Values

ValueCountFrequency (%)
6126
31.5%
596
24.0%
493
23.2%
785
21.2%

Length

2022-07-01T14:24:00.530063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T14:24:00.616794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
6126
31.5%
596
24.0%
493
23.2%
785
21.2%

Most occurring characters

ValueCountFrequency (%)
6126
31.5%
596
24.0%
493
23.2%
785
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6126
31.5%
596
24.0%
493
23.2%
785
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6126
31.5%
596
24.0%
493
23.2%
785
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6126
31.5%
596
24.0%
493
23.2%
785
21.2%

OrthNeighSize
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.34
Minimum0
Maximum19
Zeros121
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:00.690625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile12.05
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.136081227
Coefficient of variation (CV)1.238347673
Kurtosis2.282737497
Mean3.34
Median Absolute Deviation (MAD)2
Skewness1.630929123
Sum1336
Variance17.10716792
MonotonicityNot monotonic
2022-07-01T14:24:00.774401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0121
30.2%
165
16.2%
247
 
11.8%
332
 
8.0%
427
 
6.8%
522
 
5.5%
616
 
4.0%
911
 
2.8%
810
 
2.5%
710
 
2.5%
Other values (10)39
 
9.8%
ValueCountFrequency (%)
0121
30.2%
165
16.2%
247
 
11.8%
332
 
8.0%
427
 
6.8%
522
 
5.5%
616
 
4.0%
710
 
2.5%
810
 
2.5%
911
 
2.8%
ValueCountFrequency (%)
192
 
0.5%
182
 
0.5%
172
 
0.5%
163
 
0.8%
154
1.0%
143
 
0.8%
134
1.0%
125
1.2%
118
2.0%
106
1.5%

BigramFreq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21460.18253
Minimum3221.93
Maximum68055.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:00.876099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3221.93
5-th percentile7896.938
Q114415.2575
median19768.08
Q326636.9575
95-th percentile38808.521
Maximum68055.67
Range64833.74
Interquartile range (IQR)12221.7

Descriptive statistics

Standard deviation10782.09973
Coefficient of variation (CV)0.5024234866
Kurtosis3.378863088
Mean21460.18253
Median Absolute Deviation (MAD)5993.465
Skewness1.450486641
Sum8584073.01
Variance116253674.5
MonotonicityNot monotonic
2022-07-01T14:24:00.987831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25013.721
 
0.2%
29291.251
 
0.2%
25204.161
 
0.2%
8269.631
 
0.2%
21761.691
 
0.2%
27721.021
 
0.2%
26052.991
 
0.2%
60646.851
 
0.2%
9455.391
 
0.2%
15114.831
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
3221.931
0.2%
3307.121
0.2%
4308.271
0.2%
4785.51
0.2%
5155.131
0.2%
5506.731
0.2%
5880.871
0.2%
5955.391
0.2%
6248.581
0.2%
6518.461
0.2%
ValueCountFrequency (%)
68055.671
0.2%
67707.221
0.2%
64482.661
0.2%
62838.291
0.2%
62460.461
0.2%
60646.851
0.2%
59070.441
0.2%
58221.621
0.2%
57883.951
0.2%
51688.971
0.2%

TrigramFreq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2344.831775
Minimum18.34
Maximum28695.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:01.101524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18.34
5-th percentile239.4355
Q1842.98
median1751.2
Q32763.01
95-th percentile6765.454
Maximum28695.4
Range28677.06
Interquartile range (IQR)1920.03

Descriptive statistics

Standard deviation2814.404135
Coefficient of variation (CV)1.200258443
Kurtosis35.39084443
Mean2344.831775
Median Absolute Deviation (MAD)940.25
Skewness4.979486189
Sum937932.71
Variance7920870.637
MonotonicityNot monotonic
2022-07-01T14:24:01.208209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286.551
 
0.2%
2177.941
 
0.2%
1619.931
 
0.2%
1635.811
 
0.2%
3218.081
 
0.2%
75.561
 
0.2%
2927.421
 
0.2%
885.221
 
0.2%
720.371
 
0.2%
5300.741
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
18.341
0.2%
24.511
0.2%
40.611
0.2%
75.561
0.2%
78.891
0.2%
84.041
0.2%
84.361
0.2%
91.951
0.2%
112.051
0.2%
113.811
0.2%
ValueCountFrequency (%)
28695.41
0.2%
22300.111
0.2%
21745.331
0.2%
21720.471
0.2%
9196.011
0.2%
9066.991
0.2%
8920.381
0.2%
8900.711
0.2%
8856.111
0.2%
8738.111
0.2%

Frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct391
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5816.58
Minimum81
Maximum51028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:01.319909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile320.65
Q11282.5
median3155.5
Q37662.75
95-th percentile20840.1
Maximum51028
Range50947
Interquartile range (IQR)6380.25

Descriptive statistics

Standard deviation7390.535401
Coefficient of variation (CV)1.270598084
Kurtosis9.381372627
Mean5816.58
Median Absolute Deviation (MAD)2363.5
Skewness2.712438759
Sum2326632
Variance54620013.51
MonotonicityNot monotonic
2022-07-01T14:24:01.447568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17892
 
0.5%
88712
 
0.5%
41882
 
0.5%
7352
 
0.5%
55992
 
0.5%
2772
 
0.5%
25082
 
0.5%
32952
 
0.5%
38382
 
0.5%
124661
 
0.2%
Other values (381)381
95.2%
ValueCountFrequency (%)
811
0.2%
991
0.2%
1011
0.2%
1201
0.2%
1231
0.2%
1531
0.2%
1611
0.2%
1871
0.2%
1891
0.2%
1981
0.2%
ValueCountFrequency (%)
510281
0.2%
455911
0.2%
427771
0.2%
389171
0.2%
367991
0.2%
356531
0.2%
340851
0.2%
299091
0.2%
277901
0.2%
275931
0.2%

LogFreq(Zipf)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct190
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1606
Minimum2.61
Maximum5.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:01.553313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.61
5-th percentile3.1985
Q13.8075
median4.195
Q34.58
95-th percentile5.0105
Maximum5.4
Range2.79
Interquartile range (IQR)0.7725

Descriptive statistics

Standard deviation0.5507881252
Coefficient of variation (CV)0.1323818981
Kurtosis-0.2898826264
Mean4.1606
Median Absolute Deviation (MAD)0.385
Skewness-0.2857956938
Sum1664.24
Variance0.3033675589
MonotonicityNot monotonic
2022-07-01T14:24:01.658034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.446
 
1.5%
4.856
 
1.5%
4.36
 
1.5%
4.726
 
1.5%
3.925
 
1.2%
4.495
 
1.2%
4.055
 
1.2%
4.325
 
1.2%
3.795
 
1.2%
4.165
 
1.2%
Other values (180)346
86.5%
ValueCountFrequency (%)
2.611
0.2%
2.72
0.5%
2.781
0.2%
2.791
0.2%
2.881
0.2%
2.911
0.2%
2.972
0.5%
2.991
0.2%
31
0.2%
3.021
0.2%
ValueCountFrequency (%)
5.41
0.2%
5.351
0.2%
5.331
0.2%
5.291
0.2%
5.261
0.2%
5.251
0.2%
5.231
0.2%
5.171
0.2%
5.142
0.5%
5.131
0.2%

similarity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1520799273
Minimum0.007329558954
Maximum0.5481328368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:01.770734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.007329558954
5-th percentile0.04445617166
Q10.1011061096
median0.1437613396
Q30.1906218352
95-th percentile0.2947753156
Maximum0.5481328368
Range0.5408032779
Interquartile range (IQR)0.08951572562

Descriptive statistics

Standard deviation0.07673245041
Coefficient of variation (CV)0.5045534395
Kurtosis1.929525687
Mean0.1520799273
Median Absolute Deviation (MAD)0.04535981081
Skewness0.9822248161
Sum60.83197094
Variance0.005887868946
MonotonicityNot monotonic
2022-07-01T14:24:01.884397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.086792353541
 
0.2%
0.36067370331
 
0.2%
0.15754868841
 
0.2%
0.28955078621
 
0.2%
0.21500508111
 
0.2%
0.1042436611
 
0.2%
0.10517542931
 
0.2%
0.17643433621
 
0.2%
0.1296345021
 
0.2%
0.26248541661
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
0.0073295589541
0.2%
0.0080787663661
0.2%
0.0088522937151
0.2%
0.01312891581
0.2%
0.013673586771
0.2%
0.02296588571
0.2%
0.023622974751
0.2%
0.024469040331
0.2%
0.027845025371
0.2%
0.028083099981
0.2%
ValueCountFrequency (%)
0.54813283681
0.2%
0.41148601471
0.2%
0.3947753311
0.2%
0.38801104821
0.2%
0.36623635891
0.2%
0.36403296391
0.2%
0.36067370331
0.2%
0.35644788441
0.2%
0.34557764981
0.2%
0.33755066991
0.2%

AoA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct262
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8720169
Minimum2.5
Maximum15.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:01.992108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4.208
Q16.17
median7.76
Q39.5225
95-th percentile11.9075
Maximum15.56
Range13.06
Interquartile range (IQR)3.3525

Descriptive statistics

Standard deviation2.332765258
Coefficient of variation (CV)0.2963364139
Kurtosis-0.2470236004
Mean7.8720169
Median Absolute Deviation (MAD)1.69
Skewness0.2427160435
Sum3148.80676
Variance5.441793751
MonotonicityNot monotonic
2022-07-01T14:24:02.111819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.115
 
1.2%
6.055
 
1.2%
6.585
 
1.2%
95
 
1.2%
74
 
1.0%
6.894
 
1.0%
84
 
1.0%
54
 
1.0%
64
 
1.0%
5.423
 
0.8%
Other values (252)357
89.2%
ValueCountFrequency (%)
2.51
0.2%
2.91
0.2%
31
0.2%
3.231
0.2%
3.41
0.2%
3.471
0.2%
3.521
0.2%
3.561
0.2%
3.582
0.5%
3.632
0.5%
ValueCountFrequency (%)
15.561
0.2%
14.311
0.2%
14.261
0.2%
13.591
0.2%
13.391
0.2%
13.181
0.2%
132
0.5%
12.841
0.2%
12.531
0.2%
12.521
0.2%

cloze
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct113
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2755151414
Minimum0
Maximum1
Zeros125
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:02.227478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1031914894
Q30.54
95-th percentile0.9007446809
Maximum1
Range1
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.3232484173
Coefficient of variation (CV)1.173251008
Kurtosis-0.7789513316
Mean0.2755151414
Median Absolute Deviation (MAD)0.1031914894
Skewness0.8449501552
Sum110.2060565
Variance0.1044895393
MonotonicityNot monotonic
2022-07-01T14:24:02.339207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0125
31.2%
0.0221
 
5.2%
0.0212765957414
 
3.5%
0.65957446819
 
2.2%
0.76
 
1.5%
0.045
 
1.2%
0.25
 
1.2%
0.042553191495
 
1.2%
0.35
 
1.2%
0.021739130435
 
1.2%
Other values (103)200
50.0%
ValueCountFrequency (%)
0125
31.2%
0.0221
 
5.2%
0.0212765957414
 
3.5%
0.021739130435
 
1.2%
0.022222222221
 
0.2%
0.045
 
1.2%
0.042553191495
 
1.2%
0.043478260871
 
0.2%
0.064
 
1.0%
0.063829787234
 
1.0%
ValueCountFrequency (%)
11
 
0.2%
0.981
 
0.2%
0.97872340431
 
0.2%
0.97826086961
 
0.2%
0.962
0.5%
0.95744680853
0.8%
0.944
1.0%
0.93617021282
0.5%
0.923
0.8%
0.9148936172
0.5%

Plausibility
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct178
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.782090426
Minimum2.8
Maximum6.765957447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:02.447887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile4.358
Q15.468085106
median5.968085106
Q36.29787234
95-th percentile6.54
Maximum6.765957447
Range3.965957447
Interquartile range (IQR)0.829787234

Descriptive statistics

Standard deviation0.7000025904
Coefficient of variation (CV)0.12106393
Kurtosis2.054958348
Mean5.782090426
Median Absolute Deviation (MAD)0.3723404255
Skewness-1.397951111
Sum2312.83617
Variance0.4900036265
MonotonicityNot monotonic
2022-07-01T14:24:02.561611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.368
 
2.0%
5.851063838
 
2.0%
6.3829787237
 
1.8%
6.467
 
1.8%
5.7872340436
 
1.5%
6.086
 
1.5%
66
 
1.5%
6.3191489366
 
1.5%
5.5531914896
 
1.5%
6.1276595746
 
1.5%
Other values (168)334
83.5%
ValueCountFrequency (%)
2.81
0.2%
3.0638297871
0.2%
3.181
0.2%
3.3617021281
0.2%
3.681
0.2%
3.71
0.2%
3.741
0.2%
3.7446808511
0.2%
3.7872340431
0.2%
3.851063831
0.2%
ValueCountFrequency (%)
6.7659574471
 
0.2%
6.72
0.5%
6.6808510642
0.5%
6.681
 
0.2%
6.661
 
0.2%
6.643
0.8%
6.6382978722
0.5%
6.621
 
0.2%
6.61
 
0.2%
6.5957446811
 
0.2%

Position
Real number (ℝ≥0)

Distinct7
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.455
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:02.657355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q17
median8
Q39
95-th percentile11
Maximum12
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.548861657
Coefficient of variation (CV)0.1831888417
Kurtosis-0.5505915459
Mean8.455
Median Absolute Deviation (MAD)1
Skewness0.3672575643
Sum3382
Variance2.398972431
MonotonicityNot monotonic
2022-07-01T14:24:02.724146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
896
24.0%
785
21.2%
983
20.8%
1053
13.2%
637
 
9.2%
1132
 
8.0%
1214
 
3.5%
ValueCountFrequency (%)
637
 
9.2%
785
21.2%
896
24.0%
983
20.8%
1053
13.2%
1132
 
8.0%
1214
 
3.5%
ValueCountFrequency (%)
1214
 
3.5%
1132
 
8.0%
1053
13.2%
983
20.8%
896
24.0%
785
21.2%
637
 
9.2%

Predictability
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4327719026
Minimum0.01636023074
Maximum0.9999999404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:02.819915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.01636023074
5-th percentile0.1053831946
Q10.1902351975
median0.3598327637
Q30.6722501367
95-th percentile0.9327819169
Maximum0.9999999404
Range0.9836397097
Interquartile range (IQR)0.4820149392

Descriptive statistics

Standard deviation0.2815373316
Coefficient of variation (CV)0.6505443858
Kurtosis-1.056159663
Mean0.4327719026
Median Absolute Deviation (MAD)0.2048075125
Skewness0.5476675896
Sum173.108761
Variance0.07926326906
MonotonicityNot monotonic
2022-07-01T14:24:02.929596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99999994041
 
0.2%
0.24771787231
 
0.2%
0.56060934071
 
0.2%
0.75226366521
 
0.2%
0.56183475261
 
0.2%
0.13752964141
 
0.2%
0.50422805551
 
0.2%
0.13566146791
 
0.2%
0.87591189151
 
0.2%
0.20611143111
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
0.016360230741
0.2%
0.052636563781
0.2%
0.062243625521
0.2%
0.063153028491
0.2%
0.067121870821
0.2%
0.071064569061
0.2%
0.073812380431
0.2%
0.076861754061
0.2%
0.084257155661
0.2%
0.084295205771
0.2%
ValueCountFrequency (%)
0.99999994041
0.2%
0.98865836861
0.2%
0.9883741141
0.2%
0.9868566991
0.2%
0.98625266551
0.2%
0.98503965141
0.2%
0.97114688161
0.2%
0.96780878311
0.2%
0.96627163891
0.2%
0.96477824451
0.2%

PRECEDING_Frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct177
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2505782.393
Minimum11
Maximum9418422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:03.037307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile725.9
Q111875.25
median275160.5
Q34809384
95-th percentile9418422
Maximum9418422
Range9418411
Interquartile range (IQR)4797508.75

Descriptive statistics

Standard deviation3634761.17
Coefficient of variation (CV)1.45054941
Kurtosis-0.4162157297
Mean2505782.393
Median Absolute Deviation (MAD)271984.5
Skewness1.125776366
Sum1002312957
Variance1.321148876 × 1013
MonotonicityNot monotonic
2022-07-01T14:24:03.147042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
941842274
 
18.5%
480938441
 
10.2%
50256819
 
4.8%
35500011
 
2.8%
377978310
 
2.5%
5045189
 
2.2%
29500405
 
1.2%
3842985
 
1.2%
1918555
 
1.2%
6349234
 
1.0%
Other values (167)217
54.2%
ValueCountFrequency (%)
111
0.2%
631
0.2%
741
0.2%
901
0.2%
1131
0.2%
1611
0.2%
2111
0.2%
2771
0.2%
3011
0.2%
3181
0.2%
ValueCountFrequency (%)
941842274
18.5%
53272722
 
0.5%
480938441
10.2%
377978310
 
2.5%
29500405
 
1.2%
17069512
 
0.5%
15690811
 
0.2%
11414303
 
0.8%
8509401
 
0.2%
6620931
 
0.2%

PRECEDING_LogFreq(Zipf)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct140
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9237
Minimum1.77
Maximum7.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:03.256719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.77
5-th percentile3.559
Q14.7725
median6.135
Q37.38
95-th percentile7.67
Maximum7.67
Range5.9
Interquartile range (IQR)2.6075

Descriptive statistics

Standard deviation1.415330779
Coefficient of variation (CV)0.2389268158
Kurtosis-0.9715281163
Mean5.9237
Median Absolute Deviation (MAD)1.245
Skewness-0.3272292804
Sum2369.48
Variance2.003161213
MonotonicityNot monotonic
2022-07-01T14:24:03.360442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6774
 
18.5%
7.3841
 
10.2%
6.428
 
7.0%
6.2511
 
2.8%
7.2710
 
2.5%
5.536
 
1.5%
6.286
 
1.5%
5.985
 
1.2%
7.175
 
1.2%
5.135
 
1.2%
Other values (130)209
52.2%
ValueCountFrequency (%)
1.771
0.2%
2.51
0.2%
2.571
0.2%
2.651
0.2%
2.751
0.2%
2.911
0.2%
3.021
0.2%
3.141
0.2%
3.181
0.2%
3.21
0.2%
ValueCountFrequency (%)
7.6774
18.5%
7.422
 
0.5%
7.3841
10.2%
7.2710
 
2.5%
7.175
 
1.2%
6.932
 
0.5%
6.891
 
0.2%
6.753
 
0.8%
6.631
 
0.2%
6.521
 
0.2%

LENprec
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.64
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:03.448235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.727452565
Coefficient of variation (CV)0.5878130528
Kurtosis0.02557280029
Mean4.64
Median Absolute Deviation (MAD)2
Skewness0.8114518117
Sum1856
Variance7.438997494
MonotonicityNot monotonic
2022-07-01T14:24:03.529019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3118
29.5%
547
 
11.8%
141
 
10.2%
636
 
9.0%
233
 
8.2%
730
 
7.5%
430
 
7.5%
924
 
6.0%
818
 
4.5%
109
 
2.2%
Other values (3)14
 
3.5%
ValueCountFrequency (%)
141
 
10.2%
233
 
8.2%
3118
29.5%
430
 
7.5%
547
 
11.8%
636
 
9.0%
730
 
7.5%
818
 
4.5%
924
 
6.0%
109
 
2.2%
ValueCountFrequency (%)
132
 
0.5%
126
 
1.5%
116
 
1.5%
109
 
2.2%
924
6.0%
818
 
4.5%
730
7.5%
636
9.0%
547
11.8%
430
7.5%

SemD
Real number (ℝ≥0)

Distinct397
Distinct (%)100.0%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.696729559
Minimum0.7431190179
Maximum2.288068024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2022-07-01T14:24:03.620775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.7431190179
5-th percentile1.27302851
Q11.541155657
median1.701140455
Q31.86927561
95-th percentile2.110891746
Maximum2.288068024
Range1.544949006
Interquartile range (IQR)0.3281199526

Descriptive statistics

Standard deviation0.2500447016
Coefficient of variation (CV)0.1473686247
Kurtosis0.4324548838
Mean1.696729559
Median Absolute Deviation (MAD)0.1653816912
Skewness-0.3404335013
Sum673.6016348
Variance0.0625223528
MonotonicityNot monotonic
2022-07-01T14:24:03.727489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7215228351
 
0.2%
1.4631049231
 
0.2%
1.3413460461
 
0.2%
1.776317631
 
0.2%
1.8992833841
 
0.2%
1.6274628631
 
0.2%
1.9970618361
 
0.2%
1.4401465211
 
0.2%
1.5203388761
 
0.2%
1.1555377551
 
0.2%
Other values (387)387
96.8%
(Missing)3
 
0.8%
ValueCountFrequency (%)
0.74311901791
0.2%
0.80864333611
0.2%
0.9195110591
0.2%
1.0259222241
0.2%
1.1083169251
0.2%
1.110790541
0.2%
1.1366982421
0.2%
1.1478486331
0.2%
1.1555377551
0.2%
1.1686344921
0.2%
ValueCountFrequency (%)
2.2880680241
0.2%
2.2313463031
0.2%
2.2058700351
0.2%
2.1833987361
0.2%
2.1778194221
0.2%
2.1716364721
0.2%
2.1666737481
0.2%
2.1647935911
0.2%
2.1619398321
0.2%
2.1617835891
0.2%

Interactions

2022-07-01T14:23:54.913409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-07-01T14:23:04.442168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-01T14:24:04.127497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-01T14:24:04.372840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-01T14:24:04.582278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-01T14:24:04.698967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-01T14:23:57.263225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-01T14:23:57.970638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-01T14:23:58.167931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDWordConcretenessValenceArousalSensorimotorStrengthSentenceA priori PredictabilityBLP_rtBLP_accuracyOLD20#lettersOrthNeighSizeBigramFreqTrigramFreqFrequencyLogFreq(Zipf)similarityAoAclozePlausibilityPositionPredictabilityPRECEDING_FrequencyPRECEDING_LogFreq(Zipf)LENprecSemD
0100absence2.313.864.303.974638The school called because of the student's unauthorised absence for two consecutive days.0568.751.002.607018473.382537.2215583.890.0626567.700.7600006.46000090.7651743013.18122.161784
1101accent3.266.484.954.940823People from Birmingham have the most recognizable accent from the United Kingdom.0547.151.001.856221569.244700.6728844.160.1244628.600.9400005.50000080.945914111.77121.662172
2102access2.716.685.303.967939Men and women should have equal access to education and employment.1585.970.982.006016332.992611.02113334.750.0570379.100.0000006.54000070.27410440904.3151.919081
3103action2.866.006.196.226654It is time to turn ideas into action and make the plan happen.0518.131.001.856023973.258212.90249595.090.1647736.670.4400006.14000080.3947892882996.1642.097075
4104adult4.405.904.364.841120Anyone over eighteen years of age counts as an adult according to the law.0497.901.001.95508625.75647.7850994.400.2350834.680.8723406.446809100.9361845045186.4021.703787
5105advice2.735.783.054.983896That solicitor provides inexpensive legal advice to low-income families.0527.511.002.256111937.241019.99141934.850.1883268.610.7021285.85106460.749152113094.7551.952718
6106affair2.453.105.404.554314The government acted carelessly about the whole affair bringing poverty in the country.1551.181.002.50607383.06723.2139864.300.09097110.940.0200005.50000080.201068706605.5451.846129
7107aisle4.355.172.504.434396The bride walked down the aisle with her dad.0610.360.901.855119086.91334.2110373.710.2696955.950.9782616.68085160.84193194184227.6731.391624
8108alarm4.473.866.855.713259I accidentally burnt my toast, which triggered the alarm to go off.0534.111.001.955020674.181154.4637154.270.1686106.390.3404266.38297990.81608494184227.6731.860275
9109album4.696.195.635.543076The band has just released its new album which contains thirteen songs.0589.890.952.255010611.6518.3470914.550.2941776.720.7872346.34042680.8414531918555.9830.808643

Last rows

IDWordConcretenessValenceArousalSensorimotorStrengthSentenceA priori PredictabilityBLP_rtBLP_accuracyOLD20#lettersOrthNeighSizeBigramFreqTrigramFreqFrequencyLogFreq(Zipf)similarityAoAclozePlausibilityPositionPredictabilityPRECEDING_FrequencyPRECEDING_LogFreq(Zipf)LENprecSemD
390490tune3.507.003.734.709302He cannot recall the exact tune of the song any more.1528.340.981.404814357.80360.7961674.490.0918217.320.3000005.46000060.11841431334.1951.778941
391491user3.163.673.213.265457The app was developed to suit the needs of the user when ordering food.0550.470.891.754336494.942898.878013.600.1925819.570.1489366.021277110.29256094184227.6731.318902
392492value1.627.185.793.949182After a quick look, the police officer questioned the value of the item.1512.451.001.655310190.28510.96203655.000.0321666.780.0000005.160000100.06315394184227.6732.064811
393493venom4.622.935.813.023171Cobras are dangerous snakes because of the deadly venom they can inject.0621.270.972.005027322.971461.8511473.760.2661127.950.7600006.60000090.85199860874.4861.570181
394494verdict2.194.325.635.455519The jury reached a unanimous verdict and the defendant was found not guilty.0578.970.952.807023329.282352.7124644.090.33755111.050.2400006.36000060.6050964623.3691.623323
395495version1.705.303.432.783529There are some cool new features in the latest version of the software.0584.841.001.907031201.956761.0069344.540.1890778.110.0212776.127660100.248599133564.8261.823981
396496virtue1.626.704.554.181658Patience is undoubtedly Andrew's greatest virtue according to many.0583.831.002.50607711.64338.536583.510.18093011.530.2553195.97872360.418422123964.7981.864383
397497whim1.696.164.053.437621They travelled to Venice on a whim and they did not have much fun.1653.890.901.604719631.623956.122873.160.08550110.740.0000004.91489470.15242448093847.3811.945951
398498window4.866.473.275.545691The student was staring out of the window looking at the clouds.0514.621.002.206132174.712018.09137914.840.1271634.740.9574476.48936280.95935594184227.6731.628164
399499wisdom1.537.943.774.856191The young man showed great wisdom in the business despite his age.1564.420.982.856014873.61263.3916843.920.1234519.610.0000005.72340460.2433411992606.0051.867304